Fast Binary Network Intrusion Detection based on Matched Filter Optimization

As industries develop and informatization accelerates, enterprise collaboration is increasing. However, current architectures face malicious attacks, data tampering, privacy issues, and security and efficiency problems in information exchange and enterprise credibility. Additionally, the complexity of cyber threats requires integrating intelligent security measures to proactively defend against sophisticated attacks. To address these challenges, this paper introduces an intelligent and secure cloud–edge collaborative industrial information encryption strategy based on credibility assessment. The proposed strategy incorporates adaptive encryption specifically designed for cloud–edge and edge–edge architectures and utilizes attribute encryption to control access to user-downloaded data, ensuring secure information exchange. A mechanism for assessing enterprise credibility over a defined period helps maintain a trusted collaborative environment, crucial for identifying and mitigating risks from potentially malicious or unreliable entities. Furthermore, integrating intelligent threat detection and response systems enhances overall security by continuously monitoring and analyzing network traffic for anomalies. Experimental analysis evaluates the security of communication paths and examines how enterprise integrity influences collaboration outcomes. Simulation results show that this approach enhances enterprise integrity, reduces losses caused by harmful actors, and promotes efficient collaboration without compromising security. This intelligent and secure strategy not only safeguards sensitive data but also ensures the resilience and trustworthiness of the collaborative network.

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Fast Binary Network Intrusion Detection based on Matched Filter Optimization

Cited by 4 publications

References 13 publications

Effective network intrusion detection by addressing class imbalance with deep neural networks multimedia tools and applications

Effective network intrusion detection by addressing class imbalance with deep neural networks multimedia tools and applications

A Study on Network Intrusion Detection Systems by Employing Memory-Based Learning Techniques

Intelligent and Secure Cloud–Edge Collaborative Industrial Information Encryption Strategy Based on Credibility Assessment

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